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Modeling of the ARMA random effects covariance matrix in logistic random effects models
- Title
- Modeling of the ARMA random effects covariance matrix in logistic random effects models
- Authors
- Lee, Keunbaik; Jung, Hoimin; Yoo, Jae Keun
- Ewha Authors
- 유재근
- SCOPUS Author ID
- 유재근
- Issue Date
- 2019
- Journal Title
- STATISTICAL METHODS AND APPLICATIONS
- ISSN
- 1618-2510
1613-981X
- Citation
- STATISTICAL METHODS AND APPLICATIONS vol. 28, no. 2, pp. 281 - 299
- Keywords
- Cholesky decomposition; Longitudinal data; Heteroscedastic; Repeated outcomes
- Publisher
- SPRINGER HEIDELBERG
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Logistic random effects models (LREMs) have been frequently used to analyze longitudinal binary data. When a random effects covariance matrix is used to make proper inferences on covariate effects, the random effects in the models account for both within-subject association and between-subject variation, but the covariance matix is difficult to estimate because it is high-dimensional and should be positive definite. To overcome these limitations, two Cholesky decomposition approaches were proposed for precision matrix and covariance matrix: modified Cholesky decomposition and moving average Cholesky decomposition, respectively. However, the two approaches may not work when there are non-trivial and complicated correlations of repeated outcomes. In this paper, we combined the two decomposition approaches to model the random effects covariance matrix in the LREMs, thereby capturing a wider class of sophisticated dependence structures while achieving parsimony in parametrization. We then used our proposed model to analyze lung cancer data.
- DOI
- 10.1007/s10260-018-00440-y
- Appears in Collections:
- 자연과학대학 > 통계학전공 > Journal papers
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